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Real-World Evaluation of an Automated Algorithm to Detect Patients With Potentially Undiagnosed Hypertension Among Patients With Routine Care in Hawai'i.
Thompson, Mika D; Wu, Yan Yan; Nett, Blythe; Ching, Lance K; Taylor, Hermina; Lemmen, Tiffany; Sentell, Tetine L; McGurk, Meghan D; Pirkle, Catherine M.
Afiliación
  • Thompson MD; Office of Public Health Studies University of Hawai'i at Manoa Honolulu HI.
  • Wu YY; Office of Public Health Studies University of Hawai'i at Manoa Honolulu HI.
  • Nett B; Hawai'i State Department of Health Honolulu HI.
  • Ching LK; Hawai'i State Department of Health Honolulu HI.
  • Taylor H; Queens Clinically Integrated Physician Network Honolulu HI.
  • Lemmen T; Queens Clinically Integrated Physician Network Honolulu HI.
  • Sentell TL; Thompson School of Social Work and Public Health University of Hawai'i at Manoa Honolulu HI.
  • McGurk MD; Office of Public Health Studies University of Hawai'i at Manoa Honolulu HI.
  • Pirkle CM; Office of Public Health Studies University of Hawai'i at Manoa Honolulu HI.
J Am Heart Assoc ; 12(24): e031249, 2023 Dec 19.
Article en En | MEDLINE | ID: mdl-38084705
ABSTRACT

BACKGROUND:

This real-world evaluation considers an algorithm designed to detect patients with potentially undiagnosed hypertension, receiving routine care, in a large health system in Hawai'i. It quantifies patients identified as potentially undiagnosed with hypertension; summarizes the individual, clinical, and health system factors associated with undiagnosed hypertension; and examines if the COVID-19 pandemic affected detection. METHODS AND

RESULTS:

We analyzed the electronic health records of patients treated across 6 clinics from 2018 to 2021. We calculated total patients with potentially undiagnosed hypertension and compared patients flagged for undiagnosed hypertension to those with diagnosed hypertension and to the full patient panel across individual characteristics, clinical and health system factors (eg, clinic of care), and timing. Modified Poisson regression was used to calculate crude and adjusted risk ratios. Among the eligible patients (N=13 364), 52.6% had been diagnosed with hypertension, 2.7% were flagged as potentially undiagnosed, and 44.6% had no evidence of hypertension. Factors associated with a higher risk of potentially undiagnosed hypertension included individual characteristics (ages 40-84 compared with 18-39 years), clinical (lack of diabetes diagnosis) and health system factors (clinic site and being a Medicaid versus a Medicare beneficiary), and timing (readings obtained after the COVID-19 Stay-At-Home Order in Hawai'i).

CONCLUSIONS:

This evaluation provided evidence that a clinical algorithm implemented within a large health system's electronic health records could detect patients in need of follow-up to determine hypertension status, and it identified key individual characteristics, clinical and health system factors, and timing considerations that may contribute to undiagnosed hypertension among patients receiving routine care.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude Asunto principal: Pandemias / Hipertensión Límite: Aged / Humans País/Región como asunto: America do norte Idioma: En Revista: J Am Heart Assoc Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Contexto en salud: 1_ASSA2030 Problema de salud: 1_sistemas_informacao_saude Asunto principal: Pandemias / Hipertensión Límite: Aged / Humans País/Región como asunto: America do norte Idioma: En Revista: J Am Heart Assoc Año: 2023 Tipo del documento: Article
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